Free software language and environment for statistical computing and data management, visualization and analysis. It has become the language of the statisticians.
It provides a wide variety of statistical (descriptive statistics, classical statistical tests, linear and nonlinear modelling, time-series analysis, classification, clustering, …) and graphical techniques.
R is an open-source software so anyone can use, modify and write their own code and contribute to the community.
There are many resources for and by R users: rweekly.org, r-bloggers.com, … and lots of documentation online!
Source: to write your R code to a script file and save it. We can also run code in a script that is displayed in the console.
Console: to write and run R code directly.
Environments: to see the objects you create.
Output: to see the output of the executed code. The help is also displayed here.
Pieces of information that are stored in R’s environment and can be viewed, referenced, or manipulated in some way.
Examples of values that can be stored in an object include:
<- is used to assign a value to an object in RTip
We can use the # symbol to add a comment in an R script.
A single value that can be numeric, character, logical, datetime, etc.
→ Numeric
→ Character
→ Logical (TRUE/FALSE)
→ Date
An ordered collection of the same type of single value objects.
c() to create them:→ Using :
→ Using the function seq()
[]. We can use their position or their value:[]. We can use their position or their value:[]. We can use their position or their value:[]. We can use their position or their value:[]. We can use their position or their value:A factor is a special kind of character vector that contains ordered categories with an underlying order. This object stores underlying numeric values (\(1\), \(2\), \(\cdots\), \(n\)), but each of these \(n\) values has an associated character label which are called the levels of the factor.
factor() function:[1] underweight underweight normal overweight normal
Levels: normal overweight underweight
levels():level argument to specify the levels in the order we want them to appear and label to set the associated label:#Set levels in the right order
x <- factor(c("underweight", "underweight", "normal", "overweight", "normal"),
levels = c("underweight", "normal", "overweight"))
x[1] underweight underweight normal overweight normal
Levels: underweight normal overweight
#Change labels too
x <- factor(c("underweight", "underweight", "normal", "overweight", "normal"),
levels = c("underweight", "normal", "overweight"), labels = c("Underweight", "Normal", "Overweight"))
x[1] Underweight Underweight Normal Overweight Normal
Levels: Underweight Normal Overweight
A two-dimensional collection of numeric/character values indexed by pairs of integers: \(i\), \(j\)
matrix() function:cbind() and rbind(), respectively, as long as they have the same number of rows or columns.An ordered collection of different types of objects
list() function to create a list object[[]]A special type of list containing vectors of the same length. Vectors containing different types of information (numeric, character, factor, …) are stored in columns forming a tabular data structure.
$view()Tip
It is useful to use str() to see the class and structure of any object:
Tip
To quickly see the length of any vector or list, it is useful to use length():
Tip
Any object can have missing values that are stored as NA. We can check if an object is missing with the is.na() function:
Tip
Any object can have missing values that are stored as NA. We can check if an object is missing with the is.na() function:
Tip
Any object can have missing values that are stored as NA. We can check if an object is missing with the is.na() function:
Tip
Names of objects can only contains letters, numbers, _ , or . and must begin with a letter. It is recommended to use short identificative names replacing the spaces with _ .
daily_cigar.Warning
R is case sensitive, so daily_cigar is different to Daily_Cigar.
In R, basic arithmetic operations can be applied to single numeric objects, numeric vectors, numeric matrixs or dataframes with numeric columns.
We can use the arithmetic operators +, -, * or / to sum, substract, multiply or divide.
Note
When you perform an operation on a vector, the operation is automatically applied to each individual element of the vector.
< |
Less than | %in% |
Included in | |
> |
Greater than | is.na() |
Check for missing values | |
== |
Equal to | !is.na() |
Check for non-missing values | |
<= |
Less than or equal to | & |
AND | |
>= |
Greater than or equal to | | |
OR | |
!= |
Not equal to | ! |
Negation |
Note
When applied to a vector it will be applied elementwise, as before.
TRUE or FALSE. When checking the value of an object one of these two values will come up:TRUE or FALSE. When checking the value of an object one of these two values will come up:TRUE or FALSE. When checking the value of an object one of these two values will come up:TRUE or FALSE. When checking the value of an object one of these two values will come up:#Check if the value is 30 and either lower than 20 or equal to 40
x <- c(10, 15, 20, 30, 40)
x == 30 & x < 20 | x == 40[1] FALSE FALSE FALSE FALSE TRUE
Warning
Sometimes we need to use parentheses to apply the correct logical expression.
TRUE or FALSE. When checking the value of an object one of these two values will come up:#Check if the value is 30 and either lower than 20 or equal to 40
x <- c(10, 15, 20, 30, 40)
x == 30 & (x < 20 | x == 40)[1] FALSE FALSE FALSE FALSE FALSE
Note
Sometimes we have to use parentheses to apply the right logical expression.
Tip
To access a vector element, we can use logical operators:
Functions are self contained modules of code that accomplish a specific task.
sqrt() |
Square root | min() |
Minimum value | |
abs() |
Absolute value | max() |
Maximum value | |
round() |
Round value | mean() |
Mean value | |
exp() |
Exponential value | median() |
Median value | |
log(), log10() |
Logarithm of a value | sd() |
Standard deviation |
? :A package is an extension to base R that can be downloaded to provide additional functions.
As of February 2025, there are 22,120 packages available on CRAN.
To install and load any package from CRAN:
A set of R packages ideal for data management. They will make your life a lot easier.
The philosophy of tidyverse is to concatenate basic functions applied to a dataframe to accomplish complex manipulations integrated into a tidy workflow.
The tidyverse workflow is based on the usage of the pipe operator, which can be the native pipe (|>) or the magrittr pipe (%>%)
Tibbles are the tidyverse equivalent of a dataframe.
They provide better readability and usability within the tidyverse workflow.
Tibbles are the tidyverse equivalent of a dataframe.
They provide better readability and usability within the tidyverse workflow.
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5.0 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3.0 1.4 0.1 setosa
14 4.3 3.0 1.1 0.1 setosa
15 5.8 4.0 1.2 0.2 setosa
16 5.7 4.4 1.5 0.4 setosa
17 5.4 3.9 1.3 0.4 setosa
18 5.1 3.5 1.4 0.3 setosa
19 5.7 3.8 1.7 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
21 5.4 3.4 1.7 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
23 4.6 3.6 1.0 0.2 setosa
24 5.1 3.3 1.7 0.5 setosa
25 4.8 3.4 1.9 0.2 setosa
26 5.0 3.0 1.6 0.2 setosa
27 5.0 3.4 1.6 0.4 setosa
28 5.2 3.5 1.5 0.2 setosa
29 5.2 3.4 1.4 0.2 setosa
30 4.7 3.2 1.6 0.2 setosa
31 4.8 3.1 1.6 0.2 setosa
32 5.4 3.4 1.5 0.4 setosa
33 5.2 4.1 1.5 0.1 setosa
34 5.5 4.2 1.4 0.2 setosa
35 4.9 3.1 1.5 0.2 setosa
36 5.0 3.2 1.2 0.2 setosa
37 5.5 3.5 1.3 0.2 setosa
38 4.9 3.6 1.4 0.1 setosa
39 4.4 3.0 1.3 0.2 setosa
40 5.1 3.4 1.5 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
42 4.5 2.3 1.3 0.3 setosa
43 4.4 3.2 1.3 0.2 setosa
44 5.0 3.5 1.6 0.6 setosa
45 5.1 3.8 1.9 0.4 setosa
46 4.8 3.0 1.4 0.3 setosa
47 5.1 3.8 1.6 0.2 setosa
48 4.6 3.2 1.4 0.2 setosa
49 5.3 3.7 1.5 0.2 setosa
50 5.0 3.3 1.4 0.2 setosa
51 7.0 3.2 4.7 1.4 versicolor
52 6.4 3.2 4.5 1.5 versicolor
53 6.9 3.1 4.9 1.5 versicolor
54 5.5 2.3 4.0 1.3 versicolor
55 6.5 2.8 4.6 1.5 versicolor
56 5.7 2.8 4.5 1.3 versicolor
57 6.3 3.3 4.7 1.6 versicolor
58 4.9 2.4 3.3 1.0 versicolor
59 6.6 2.9 4.6 1.3 versicolor
60 5.2 2.7 3.9 1.4 versicolor
61 5.0 2.0 3.5 1.0 versicolor
62 5.9 3.0 4.2 1.5 versicolor
63 6.0 2.2 4.0 1.0 versicolor
64 6.1 2.9 4.7 1.4 versicolor
65 5.6 2.9 3.6 1.3 versicolor
66 6.7 3.1 4.4 1.4 versicolor
67 5.6 3.0 4.5 1.5 versicolor
68 5.8 2.7 4.1 1.0 versicolor
69 6.2 2.2 4.5 1.5 versicolor
70 5.6 2.5 3.9 1.1 versicolor
71 5.9 3.2 4.8 1.8 versicolor
72 6.1 2.8 4.0 1.3 versicolor
73 6.3 2.5 4.9 1.5 versicolor
74 6.1 2.8 4.7 1.2 versicolor
75 6.4 2.9 4.3 1.3 versicolor
76 6.6 3.0 4.4 1.4 versicolor
77 6.8 2.8 4.8 1.4 versicolor
78 6.7 3.0 5.0 1.7 versicolor
79 6.0 2.9 4.5 1.5 versicolor
80 5.7 2.6 3.5 1.0 versicolor
81 5.5 2.4 3.8 1.1 versicolor
82 5.5 2.4 3.7 1.0 versicolor
83 5.8 2.7 3.9 1.2 versicolor
84 6.0 2.7 5.1 1.6 versicolor
85 5.4 3.0 4.5 1.5 versicolor
86 6.0 3.4 4.5 1.6 versicolor
87 6.7 3.1 4.7 1.5 versicolor
88 6.3 2.3 4.4 1.3 versicolor
89 5.6 3.0 4.1 1.3 versicolor
90 5.5 2.5 4.0 1.3 versicolor
91 5.5 2.6 4.4 1.2 versicolor
92 6.1 3.0 4.6 1.4 versicolor
93 5.8 2.6 4.0 1.2 versicolor
94 5.0 2.3 3.3 1.0 versicolor
95 5.6 2.7 4.2 1.3 versicolor
96 5.7 3.0 4.2 1.2 versicolor
97 5.7 2.9 4.2 1.3 versicolor
98 6.2 2.9 4.3 1.3 versicolor
99 5.1 2.5 3.0 1.1 versicolor
100 5.7 2.8 4.1 1.3 versicolor
101 6.3 3.3 6.0 2.5 virginica
102 5.8 2.7 5.1 1.9 virginica
103 7.1 3.0 5.9 2.1 virginica
104 6.3 2.9 5.6 1.8 virginica
105 6.5 3.0 5.8 2.2 virginica
106 7.6 3.0 6.6 2.1 virginica
107 4.9 2.5 4.5 1.7 virginica
108 7.3 2.9 6.3 1.8 virginica
109 6.7 2.5 5.8 1.8 virginica
110 7.2 3.6 6.1 2.5 virginica
111 6.5 3.2 5.1 2.0 virginica
112 6.4 2.7 5.3 1.9 virginica
113 6.8 3.0 5.5 2.1 virginica
114 5.7 2.5 5.0 2.0 virginica
115 5.8 2.8 5.1 2.4 virginica
116 6.4 3.2 5.3 2.3 virginica
117 6.5 3.0 5.5 1.8 virginica
118 7.7 3.8 6.7 2.2 virginica
119 7.7 2.6 6.9 2.3 virginica
120 6.0 2.2 5.0 1.5 virginica
121 6.9 3.2 5.7 2.3 virginica
122 5.6 2.8 4.9 2.0 virginica
123 7.7 2.8 6.7 2.0 virginica
124 6.3 2.7 4.9 1.8 virginica
125 6.7 3.3 5.7 2.1 virginica
126 7.2 3.2 6.0 1.8 virginica
127 6.2 2.8 4.8 1.8 virginica
128 6.1 3.0 4.9 1.8 virginica
129 6.4 2.8 5.6 2.1 virginica
130 7.2 3.0 5.8 1.6 virginica
131 7.4 2.8 6.1 1.9 virginica
132 7.9 3.8 6.4 2.0 virginica
133 6.4 2.8 5.6 2.2 virginica
134 6.3 2.8 5.1 1.5 virginica
135 6.1 2.6 5.6 1.4 virginica
136 7.7 3.0 6.1 2.3 virginica
137 6.3 3.4 5.6 2.4 virginica
138 6.4 3.1 5.5 1.8 virginica
139 6.0 3.0 4.8 1.8 virginica
140 6.9 3.1 5.4 2.1 virginica
141 6.7 3.1 5.6 2.4 virginica
142 6.9 3.1 5.1 2.3 virginica
143 5.8 2.7 5.1 1.9 virginica
144 6.8 3.2 5.9 2.3 virginica
145 6.7 3.3 5.7 2.5 virginica
146 6.7 3.0 5.2 2.3 virginica
147 6.3 2.5 5.0 1.9 virginica
148 6.5 3.0 5.2 2.0 virginica
149 6.2 3.4 5.4 2.3 virginica
150 5.9 3.0 5.1 1.8 virginica
Tibbles are the tidyverse equivalent of a dataframe.
They provide better readability and usability within the tidyverse workflow:
# A tibble: 150 × 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# ℹ 140 more rows
filter_iris <- subset(iris, Species == "setosa")
sel_filter_iris <- filter_iris[, c("Sepal.Length", "Sepal.Width")]
sel_filter_iris$Sepal.Size <- ifelse(sel_filter_iris$Sepal.Length > mean(sel_filter_iris$Sepal.Length) & sel_filter_iris$Sepal.Width > mean(sel_filter_iris$Sepal.Width), 2, 1)
sel_filter_iris$Sepal.Size <- factor(sel_filter_iris$Sepal.Size, levels = 1:2, labels = c("Small", "Big"))
small_iris <- sel_filter_iris[sel_filter_iris$Sepal.Size == "Small",]
small_sepal_area <- mean(small_iris[,"Sepal.Length"] * small_iris[,"Sepal.Width"])
big_iris <- sel_filter_iris[sel_filter_iris$Sepal.Size == "Big",]
big_sepal_area <- mean(big_iris[,"Sepal.Length"] * big_iris[,"Sepal.Width"])
data.frame(
"Sepal.Size" = c("Small", "Big"),
"Sepal.Area" = c(small_sepal_area, big_sepal_area)
) Sepal.Size Sepal.Area
1 Small 15.65636
2 Big 20.36647
library(dplyr)
iris |>
filter(Species == "setosa") |>
select(Sepal.Length, Sepal.Width) |>
mutate(
Sepal.Size = case_when(
Sepal.Length > mean(Sepal.Length) & Sepal.Width > mean(Sepal.Width) ~ 2,
.default = 1
),
Sepal.Size = factor(Sepal.Size, levels = 1:2, labels = c("Small", "Big"))
) |>
group_by(Sepal.Size) |>
summarise(
Sepal.Area = mean(Sepal.Length*Sepal.Width)
)# A tibble: 2 × 2
Sepal.Size Sepal.Area
<fct> <dbl>
1 Small 15.7
2 Big 20.4
In comparison with base R, the tidyverse provides:
Consistent & Readable Syntax: uses a uniform and intuitive grammar (e.g., |> pipe operator).
Efficient Data Manipulation: specific functions that simplify a lot data wrangling and reduce the risk of mistakes.
Better Data Visualization: {ggplot2} is the most powerful and popular tool for creating graphics in R.
Easier to Learn for Beginners: more human-readable than base R’s indexing and function chaining.
Applied Biostatistics Course with R